|
--- |
|
base_model: haryoaw/scenario-TCR-NER_data-univner_half |
|
library_name: transformers |
|
license: mit |
|
metrics: |
|
- precision |
|
- recall |
|
- f1 |
|
- accuracy |
|
tags: |
|
- generated_from_trainer |
|
model-index: |
|
- name: scenario-kd-po-ner-full-xlmr_data-univner_half44 |
|
results: [] |
|
--- |
|
|
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
|
should probably proofread and complete it, then remove this comment. --> |
|
|
|
# scenario-kd-po-ner-full-xlmr_data-univner_half44 |
|
|
|
This model is a fine-tuned version of [haryoaw/scenario-TCR-NER_data-univner_half](https://huggingface.co/haryoaw/scenario-TCR-NER_data-univner_half) on the None dataset. |
|
It achieves the following results on the evaluation set: |
|
- Loss: 53.5855 |
|
- Precision: 0.7926 |
|
- Recall: 0.7941 |
|
- F1: 0.7934 |
|
- Accuracy: 0.9792 |
|
|
|
## Model description |
|
|
|
More information needed |
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 3e-05 |
|
- train_batch_size: 8 |
|
- eval_batch_size: 32 |
|
- seed: 44 |
|
- gradient_accumulation_steps: 4 |
|
- total_train_batch_size: 32 |
|
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
|
- lr_scheduler_type: linear |
|
- num_epochs: 10 |
|
|
|
### Training results |
|
|
|
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | |
|
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:| |
|
| 94.1316 | 0.5828 | 500 | 77.7639 | 0.7573 | 0.7254 | 0.7410 | 0.9749 | |
|
| 69.1816 | 1.1655 | 1000 | 70.0861 | 0.7700 | 0.7553 | 0.7626 | 0.9771 | |
|
| 62.5325 | 1.7483 | 1500 | 66.1010 | 0.7697 | 0.7728 | 0.7712 | 0.9774 | |
|
| 58.8032 | 2.3310 | 2000 | 63.2843 | 0.7722 | 0.7813 | 0.7767 | 0.9781 | |
|
| 55.8439 | 2.9138 | 2500 | 61.3899 | 0.7711 | 0.7839 | 0.7774 | 0.9777 | |
|
| 53.6386 | 3.4965 | 3000 | 59.5744 | 0.7829 | 0.7804 | 0.7816 | 0.9782 | |
|
| 51.8854 | 4.0793 | 3500 | 58.4745 | 0.7896 | 0.7831 | 0.7864 | 0.9784 | |
|
| 50.3704 | 4.6620 | 4000 | 57.3648 | 0.7888 | 0.7917 | 0.7902 | 0.9787 | |
|
| 49.2945 | 5.2448 | 4500 | 56.3673 | 0.8003 | 0.7807 | 0.7904 | 0.9787 | |
|
| 48.3678 | 5.8275 | 5000 | 55.7695 | 0.7906 | 0.7840 | 0.7873 | 0.9787 | |
|
| 47.4721 | 6.4103 | 5500 | 55.1454 | 0.7836 | 0.7964 | 0.7900 | 0.9792 | |
|
| 46.9783 | 6.9930 | 6000 | 54.6410 | 0.7931 | 0.7976 | 0.7953 | 0.9790 | |
|
| 46.3896 | 7.5758 | 6500 | 54.2132 | 0.8004 | 0.7902 | 0.7953 | 0.9792 | |
|
| 45.895 | 8.1585 | 7000 | 53.9535 | 0.7906 | 0.7945 | 0.7925 | 0.9792 | |
|
| 45.6796 | 8.7413 | 7500 | 53.7738 | 0.7918 | 0.7895 | 0.7906 | 0.9788 | |
|
| 45.4159 | 9.3240 | 8000 | 53.6266 | 0.7904 | 0.7950 | 0.7927 | 0.9793 | |
|
| 45.3274 | 9.9068 | 8500 | 53.5855 | 0.7926 | 0.7941 | 0.7934 | 0.9792 | |
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.44.2 |
|
- Pytorch 2.1.1+cu121 |
|
- Datasets 2.14.5 |
|
- Tokenizers 0.19.1 |
|
|
